Antoine Manzanera

A new motion detection algorithm based on Σ-Δ background estimation

Motion detection using a stationary camera can be done by estimating the static scene (background). In that purpose, we propose a new method based on a simple recursive non linear operator, the @[emailxa0protected] filter. Used along with a spatiotemporal regularization algorithm, it allows robust, computationally efficient and accurate motion detection. To deal with complex scenes containing a wide range of motion models with very different time constants, we propose a generalization of the basic model to multiple @[emailxa0protected] estimation.

Medial faces from a concise 3D thinning algorithm

We propose in this paper a new 3D fully parallel thinning algorithm that we believe to be the most concise due to its simple characterization. The algorithm is indeed completely defined by a set of five patterns, three removing conditions and two non-removing conditions. These patterns are designed from the two fundamental and compatible constraints usually expected in skeleta: (1) Topology preservation and (2) Medial surface. From these two constraints, the removing patterns terns (/spl alpha//sub 1/, /spl alpha//sub 2/ and /spl alpha//sub 3/) detect the non-local maxima, whereas the non-removing patterns (/spl beta//sub 1/ and /spl beta//sub 2/) prevent any topology change that the removing conditions could imply. We show that the three mentioned constraints are respected. The logical conciseness of our procedure, called MB-3D, makes it to our knowledge the easiest 3D thinning algorithm to implement. Some results are displayed, that illustrate the relevance of our approach.

Improved low complexity fully parallel thinning algorithm

A fully parallel iterative thinning algorithm called MB2 is presented. It favourably competes with the best known algorithms regarding homotopy, mediality, thickness, rotation invariance and noise immunity, while featuring a speed improvement by a factor of two or more owing to a smaller number of operations to perform. MB2 is grounded on a simple physics-based thinning principle that conveys both quality, efficiency and conceptual clarity. It is particularly suited to data parallel execution.

Σ-Δ background subtraction and the Zipf law

The fibrous structure of a non-woven mat is disrupted by an apparatus comprising at least one roll which has one or more upraised ribs in a chevron pattern extending helically around the roll. In processing, the mat passes under tension over at least a portion of at least one chevron roll and preferably over two chevron rolls which may or may not be intermeshing. Thereafter, the mat may be crushed or crimped by being fed through to intermeshing third and fourth chevron rolls. Additional rolls may be supplied to exert tension. The mat which exits from the apparatus has a disrupted fibrous structure and is a superior skeleton within a structure such as a polymeric foam board.

n-dimensional skeletonization: a unified mathematical framework

We present a skeletonization algorithm defined by ex- plicit Boolean conditions which are dimension independent. The pro- posed procedure leads to new thinning algorithms in two dimen- sions (2D) and three dimensions (3D). We establish the mathematical properties of the resulting skeleton referred to as the MB skeleton. From a topological point of view, we prove that the algorithm preserves connectivity in 2D and 3D. From a metric point of view, we show that the MB skeleton is located on a median hy- persurface (MHS) that we define. This MHS does not correspond to the standard notion of median axis/surface in 2D/3D, as it combines the various distances associated with the hypercubic grid. The MHS specificities prove to make the skeleton robust with respect to noise and rotation. Then we present the algorithmic properties of the MB skeleton: First, the algorithm is fully parallel, which means that no spatial subiterations are needed. This property, together with the symmetry of the Boolean n-dimensional patterns, leads to a per- fectly isotropic skeleton. Second, we emphasize the extreme con- ciseness of the Boolean expression, and derive the computational efficiency of the procedure.

Motion detection: Fast and robust algorithms for embedded systems

This article introduces a new hierarchical version of a set of motion detection algorithms called ΣΔ. These new algorithms are designed to preserve as much as possible the computational efficiency of the basic ΣΔ estimation, in order to target real-time implementation for low power consumption processors and embedded systems.

High performance motion detection: some trends toward new embedded architectures for vision systems

The goal of this article is to compare some optimised implementations on current high performance platforms in order to highlight architectural trends in the field of embedded architectures and to get an estimation of what should be the components of a next generation vision system. We present some implementations of robust motion detection algorithms on three architectures: a general purpose RISC processor—the PowerPC G4—a parallel artificial retina dedicated to low level image processing—Pvlsar34—and the Associative Mesh, a specialized architecture based on associative net. To handle the different aspects and constraints of embedded systems, execution time and power consumption of these architectures are compared.

A NEW HYBRID DIFFERENTIAL FILTER FOR MOTION DETECTION

A new operator to compute time differentiation in an image sequence is pre- sented. It is founded on hybrid filters combining morphological and linear recur- sive operations. It estimates recursively the amplitude of time-variation within a certain interval. It combines the change detection capability of the temporal morphological gradient, and the (exponential) smoothing effect of the linear re- cursive average. It is particularly suited to small and low amplitude motion. We show how to use this filter within an adaptive motion detection algorithm.

A coronary artery segmentation method based on multiscale analysis and region growing.

Accurate coronary artery segmentation is a fundamental step in various medical imaging applications such as stenosis detection, 3D reconstruction and cardiac dynamics assessing. In this paper, a multiscale region growing (MSRG) method for coronary artery segmentation in 2D X-ray angiograms is proposed. First, a region growing rule incorporating both vesselness and direction information in a unique way is introduced. Then an iterative multiscale search based on this criterion is performed. Selected points in each step are considered as seeds for the following step. By combining vesselness and direction information in the growing rule, this method is able to avoid blockage caused by low vesselness values in vascular regions, which in turn, yields continuous vessel tree. Performing the process in a multiscale fashion helps to extract thin and peripheral vessels often missed by other segmentation methods. Quantitative evaluation performed on real angiography images shows that the proposed segmentation method identifies about 80% of the total coronary artery tree in relatively easy images and 70% in challenging cases with a mean precision of 82% and outperforms others segmentation methods in terms of sensitivity. The MSRG segmentation method was also implemented with different enhancement filters and it has been shown that the Frangi filter gives better results. The proposed segmentation method has proven to be tailored for coronary artery segmentation. It keeps an acceptable performance when dealing with challenging situations such as noise, stenosis and poor contrast.

Ultra-Fast Skeleton Based on an Isotropic Fully Parallel Algorithm

In this paper we introduce a new thinning algorithm, called MB, which is optimized with respect to the total number of elementary Boolean operators needed to perform it. We first emphasize the sound foundations of the algorithm, which is built by expressing into the Boolean language the three following constraints: (1) homotopy, (2) median axis and (3) isotropy. The MB algorithm benefits from both novel algorithmic ideas and systematic logic minimization. By hunting down any redundancy in the expressions of topological/geometrical features, we achieve a procedure that is: firstly, dramatically low-cost, as it is completely computed in 18 Boolean binary operators per iteration, and secondly, fully parallel, or one-single-pass, which guarantees that the number of iterations equals half the biggest object thickness.